Bayesian analysis of astrophysical and laboratory data favors the two-families scenario of coexisting hadronic and strange quark stars over the one-family scenario.
The equation of state for dense nucleonic matter from a metamodeling. I. Foundational aspects
10 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
A metamodeling for the nucleonic equation of state (EOS), inspired from a Taylor expansion around the saturation density of symmetric nuclear matter, is proposed and parameterized in terms of the empirical parameters. The present knowledge of nuclear empirical parameters is first reviewed in order to estimate their average values and associated uncertainties, and thus defining the parameter space of the metamodeling. They are divided into isoscalar and isovector type, and ordered according to their power in the density expansion. The goodness of the metamodeling is analyzed against the predictions of the original models. In addition, since no correlation among the empirical parameters is assumed a priori, all arbitrary density dependences can be explored, which might not be accessible in existing functionals. Spurious correlations due to the assumed functional form are also removed. This meta-EOS allows direct relations between the uncertainties on the empirical parameters and the density dependence of the nuclear equation of state and its derivatives, and the mapping between the two can be done with standard Bayesian techniques. A sensitivity analysis shows that the more influential empirical parameters are the isovector parameters $L_{sym}$ and $K_{sym}$, and that laboratory constraints at super-saturation densities are essential to reduce the present uncertainties. The present metamodeling for the EOS for nuclear matter is proposed for further applications in neutron stars and supernova matter.
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representative citing papers
Bayesian NS EoS study using full nuclear posterior distributions and consistent crust modeling finds increased surface thickness and crustal moment of inertia relative to prior work.
A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
Emulator-assisted Bayesian inference of an extended Skyrme EDF, jointly constrained by nuclear observables, ab initio calculations, and NICER data, produces posteriors yielding consistent neutron star crust and core properties with a provided multivariate Gaussian for bulk nuclear matter parameters.
RMF-CC models with ωρ coupling better match multi-messenger NS data and LQCD/NEP constraints than the baseline, yet standard RMF remains preferred without core phase transitions, requiring high Ksat ~300 MeV.
Hybrid neutron-star equations of state remain sensitive to the low-density nucleonic model at transition densities around 2ρ₀, with model spread in radius and tidal deformability exceeding observational uncertainty by factors of ~1.8 and ~1.4.
Bayesian modeling with informed priors reduces uncertainties in neutron-star crust shear properties, predicting torsional mode frequencies of 20-50 Hz compatible with observations.
The MUSES Calliope engine computes multi-dimensional QCD equations of state, merges them consistently, and feeds them into viscous hydrodynamic simulations of heavy-ion collisions with movable critical points and critical scaling in transport coefficients.
The paper evaluates how triangular versus two-L-shaped geometries, arm lengths, and presence of low-frequency instruments affect the science reach of the Einstein Telescope for compact binaries, multi-messenger events, and stochastic backgrounds.
Review summarizing the role of dense-matter equation of state, weak interactions, and r-process nucleosynthesis in binary neutron star mergers and their multimessenger observables.
citing papers explorer
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Is the coexistence of strange quark stars and hadronic stars favored by astrophysical data? A Bayesian analysis
Bayesian analysis of astrophysical and laboratory data favors the two-families scenario of coexisting hadronic and strange quark stars over the one-family scenario.
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Properties of the neutron star crust informed by nuclear structure data
Bayesian NS EoS study using full nuclear posterior distributions and consistent crust modeling finds increased surface thickness and crustal moment of inertia relative to prior work.
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A Physics Informed Bayesian Neural Network for the Neutron Star Equation of State
A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
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Emulator-Assisted Nuclear DFT Inference and Its Consequences for the Structure of Neutron Stars
Emulator-assisted Bayesian inference of an extended Skyrme EDF, jointly constrained by nuclear observables, ab initio calculations, and NICER data, produces posteriors yielding consistent neutron star crust and core properties with a provided multivariate Gaussian for bulk nuclear matter parameters.
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Relativistic Mean Field Approach with Chiral Symmetry Breaking and Quark Confinement in the light of Astrophysical Observations
RMF-CC models with ωρ coupling better match multi-messenger NS data and LQCD/NEP constraints than the baseline, yet standard RMF remains preferred without core phase transitions, requiring high Ksat ~300 MeV.
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Sensitivity of Neutron Star Observables to Transition Density in Hybrid Equation-of-State Models
Hybrid neutron-star equations of state remain sensitive to the low-density nucleonic model at transition densities around 2ρ₀, with model spread in radius and tidal deformability exceeding observational uncertainty by factors of ~1.8 and ~1.4.
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Bayesian analysis of the shear modulus in the neutron-star crust
Bayesian modeling with informed priors reduces uncertainties in neutron-star crust shear properties, predicting torsional mode frequencies of 20-50 Hz compatible with observations.
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Studying the QCD Matter produced in Heavy-Ion Collisions using the MUSES Calculation Engine
The MUSES Calliope engine computes multi-dimensional QCD equations of state, merges them consistently, and feeds them into viscous hydrodynamic simulations of heavy-ion collisions with movable critical points and critical scaling in transport coefficients.
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Science with the Einstein Telescope: a comparison of different designs
The paper evaluates how triangular versus two-L-shaped geometries, arm lengths, and presence of low-frequency instruments affect the science reach of the Einstein Telescope for compact binaries, multi-messenger events, and stochastic backgrounds.
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Nuclear Physics of Binary Neutron Star Mergers
Review summarizing the role of dense-matter equation of state, weak interactions, and r-process nucleosynthesis in binary neutron star mergers and their multimessenger observables.